---
language:
- en
license: cc-by-4.0
size_categories:
- n<1K
task_categories:
- text-classification
task_ids:
- emotion-classification
tags:
- pragmatic-reasoning
- theory-of-mind
- emotion-inference
- indirect-speech
- benchmark
- multi-annotator
- plutchik-emotions
- vad-dimensions
dataset_info:
  features:
  - name: id
    dtype: int64
  - name: subtype
    dtype: string
  - name: context
    dtype: string
  - name: speaker
    dtype: string
  - name: listener
    dtype: string
  - name: utterance
    dtype: string
  - name: power_relation
    dtype: string
  - name: gold_standard
    dtype: string
  - name: ann1_emotion
    dtype: string
  - name: ann2_emotion
    dtype: string
  - name: ann3_emotion
    dtype: string
  - name: valence_mean
    dtype: float64
  - name: arousal_mean
    dtype: float64
  - name: dominance_mean
    dtype: float64
  splits:
  - name: train
    num_examples: 211
  - name: validation
    num_examples: 48
  - name: test
    num_examples: 41
---

# CEI: A Benchmark for Evaluating Pragmatic Reasoning in Language Models

## Dataset Description

CEI (Contextual Emotional Inference) is a benchmark of 300 expert-authored scenarios for evaluating how well language models interpret pragmatically complex utterances in social contexts. Each scenario presents a communicative exchange involving indirect speech (sarcasm, mixed signals, strategic politeness, passive aggression, or deflection) where the speaker's literal words diverge from their actual emotional state.

- **Paper:** CEI: A Benchmark for Evaluating Pragmatic Reasoning in Language Models (DMLR 2026)
- **Repository:** https://github.com/jon-chun/cei-tom-dataset-base
- **Zenodo:** https://doi.org/10.5281/zenodo.18528705
- **License:** CC-BY-4.0 (data), MIT (code)

## Dataset Structure

### Scenarios
- **300 scenarios** across 5 pragmatic subtypes (60 each)
- **3 independent annotations** per scenario (900 total)
- **Predefined splits:** train (211), validation (48), test (41), stratified by subtype and power relation

### Fields

| Field | Type | Description |
|-------|------|-------------|
| `id` | int | Scenario ID (unique within subtype) |
| `subtype` | string | Pragmatic subtype (sarcasm-irony, mixed-signals, strategic-politeness, passive-aggression, deflection-misdirection) |
| `context` | string | Situational context (2-4 sentences) |
| `speaker` | string | Speaker's role in the scenario |
| `listener` | string | Listener's role in the scenario |
| `utterance` | string | The speaker's pragmatically ambiguous utterance |
| `power_relation` | string | Power dynamic: peer, high-to-low, or low-to-high |
| `gold_standard` | string | Gold-standard emotion (majority vote + expert adjudication) |
| `ann1_emotion` | string | Annotator 1's emotion label (Plutchik) |
| `ann2_emotion` | string | Annotator 2's emotion label (Plutchik) |
| `ann3_emotion` | string | Annotator 3's emotion label (Plutchik) |
| `valence_mean` | float | Mean valence rating across annotators (-1.0 to +1.0) |
| `arousal_mean` | float | Mean arousal rating across annotators (-1.0 to +1.0) |
| `dominance_mean` | float | Mean dominance rating across annotators (-1.0 to +1.0) |

### Pragmatic Subtypes
| Subtype | Description | Fleiss' kappa |
|---------|-------------|---------------|
| Sarcasm/Irony | Speaker says the opposite of what they mean | 0.25 |
| Passive Aggression | Hostility expressed through superficial compliance | 0.22 |
| Strategic Politeness | Polite language masking negative intent | 0.20 |
| Mixed Signals | Contradictory verbal and contextual cues | 0.16 |
| Deflection/Misdirection | Speaker redirects to avoid revealing feelings | 0.06 |

### Labels
- **Primary emotion:** One of Plutchik's 8 basic emotions (joy, trust, fear, surprise, sadness, disgust, anger, anticipation)
- **VAD ratings:** Mean Valence, Arousal, Dominance across 3 annotators, mapped to [-1.0, +1.0]
- **Gold standard:** Majority vote with expert adjudication for three-way splits

### Power Relations
- Peer (72%), High-to-Low authority (20%), Low-to-High authority (7%)

## Key Statistics
- **Inter-annotator agreement:** Overall kappa = 0.21 (fair), ranging from 0.06 (deflection) to 0.25 (sarcasm)
- **Human accuracy (vs. gold):** 61% mean, 14.3% unanimous, 31.3% three-way split
- **Best LLM baseline:** 25.0% accuracy (Llama-3.1-70B, zero-shot) vs. 54% human majority agreement
- **Random baseline:** 12.5% (8-class)

## Intended Uses
- Benchmarking LLM pragmatic reasoning capabilities
- Diagnosing model failure modes on indirect speech subtypes
- Research on emotion inference, social AI, Theory of Mind
- Soft-label training using per-annotator distributions

## Limitations
- All scenarios are expert-authored (not naturalistic)
- English only
- 15 undergraduate annotators from a single institution
- Small scale (300 scenarios) optimized for annotation quality over quantity

## Citation
```bibtex
@article{chun2026cei,
  title={CEI: A Benchmark for Evaluating Pragmatic Reasoning in Language Models},
  author={Chun, Jon and Sussman, Hannah and Pechon-Elkins, Mateo and Mangine, Adrian and Kocaman, Murathan and Sidorko, Kirill and Koirala, Abhigya and McCloud, Andre and Akanwe, Wisdom and Gassama, Moustapha and Enright, Anne-Duncan and Dunson, Peter and Ng, Tiffanie and von Rosenstiel, Anna and Idowu, Godwin},
  journal={Journal of Data-centric Machine Learning Research (DMLR)},
  year={2026}
}
```